Uncertainty-Aware Graph Neural Network for Semi-Supervised Diversified Recommendation

Uncertainty-Aware Graph Neural Network for Semi-Supervised Diversified Recommendation | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Uncertainty-Aware Graph Neural Network for Semi-Supervised Diversified Recommendation Minjie This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3263186/v2 This work is licensed under a CC BY 4.0 License Status: Posted Version 2 posted You are reading this latest preprint version Show more versions Abstract Graphs are a powerful tool for representing structured and relational data in various domains, including social networks, knowledge graphs, and molecular structures. Semi-supervised learning on graphs has emerged as a promising approach to address real-world challenges and applications. In this paper, we propose an uncertainty-aware pseudo-label selection framework for promoting diversity learning in recommendation systems. Our approach harnesses the power of semi-supervised Graph Neural Networks (GNNs), utilizing both labeled and unlabeled data, to address data sparsity issues often encountered in real-world recommendation scenarios. Pseudo-labeling, a prevalent semi-supervised method, combats label scarcity by enhancing the training set with high-confidence pseudolabels for unlabeled nodes, enabling self-training cycles for supervised models. By incorporating pseudo-labels selected based on the model’s uncertainty, our framework is designed to improve the model’s generalization and foster diverse recommendations. The main contributions of this paper include introducing the uncertainty-aware pseudo-label selection framework, providing a comprehensive description of the framework, and presenting an experimental evaluation comparing its performance against baseline methods in terms of recommendation quality and diversity. Our proposed method demonstrates the effectiveness of uncertainty-aware pseudo-label selection in enhancing the diversity of recommendation systems and delivering a more engaging, personalized, and diverse set of suggestions for users. recommendation systems uncertainty-aware pseudo-label selection diversity recommendation Full Text Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 2 posted You are reading this latest preprint version Show more versions Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3263186","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":271863719,"identity":"f9488224-497f-411b-b923-c0ab13eb5864","order_by":0,"name":"Minjie","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAzElEQVRIiWNgGAWjYBACg/NnDD7+/GPHw8/e/vzzzz8McvbtB/BrsTzMZ2CQ2JAsJ9lzsI2ZsYHB2OBMAn4t9of5QVoOGhvcSARrSdxwg4DDzCBaDiRuuPmQaC28GwwSfwC13E5sf/zxB0Pi/hlEaWkAa4HYsk/iAX4tBod5PoC01G+4CfF+4maJBIJaikDeB3qBEaJl4wzCWooTgIGcOLMH7DAJwoEMjErDg8CoTOxnPw6KShs5g+MH8GtBBxKkKR8Fo2AUjIJRgB0AAEPRXHDeYof+AAAAAElFTkSuQmCC","orcid":"","institution":"","correspondingAuthor":true,"prefix":"","firstName":"","middleName":"","lastName":"Minjie","suffix":""}],"badges":[],"createdAt":"2023-08-14 14:59:27","currentVersionCode":2,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-3263186/v2","doiUrl":"https://doi.org/10.21203/rs.3.rs-3263186/v2","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":50929565,"identity":"5904b525-634b-47d5-8db0-68ec80278ea4","added_by":"auto","created_at":"2024-02-09 17:45:33","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":621448,"visible":true,"origin":"","legend":"","description":"","filename":"UGNNRevised.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3263186/v2_covered_833564e0-4324-4cf1-b4cb-55791c3e1c41.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"Uncertainty-Aware Graph Neural Network for Semi-Supervised Diversified Recommendation","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"recommendation systems, uncertainty-aware pseudo-label selection, diversity recommendation","lastPublishedDoi":"10.21203/rs.3.rs-3263186/v2","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3263186/v2","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eGraphs are a powerful tool for representing structured and relational data in various domains, including social networks, knowledge graphs, and molecular structures. Semi-supervised learning on graphs has emerged as a promising approach to address real-world challenges and applications. In this paper, we propose an uncertainty-aware pseudo-label selection framework for promoting diversity learning in recommendation systems. Our approach harnesses the power of semi-supervised Graph Neural Networks (GNNs), utilizing both labeled and unlabeled data, to address data sparsity issues often encountered in real-world recommendation scenarios. Pseudo-labeling, a prevalent semi-supervised method, combats label scarcity by enhancing the training set with high-confidence pseudolabels for unlabeled nodes, enabling self-training cycles for supervised models. By incorporating pseudo-labels selected based on the model’s uncertainty, our framework is designed to improve the model’s generalization and foster diverse recommendations. 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